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Record W3195793045 · doi:10.1002/9781394260591.ch12

Assessing Criminal Responsibility

2013· other· en· W3195793045 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicTorture, Ethics, and Law
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsJuryCriminal responsibilityWitnessPsychologyInsanityPolitical scienceCriminologyLawCriminal law

Abstract

fetched live from OpenAlex

In this chapter, the authors focus on three major areas: insanity standards and the construal of criminal responsibility, a review of issues related to the assessment of criminal responsibility and an overview of the empirical developments regarding criminal responsibility. The complexity of arguments, philosophical debates, opinions, and data on the insanity defense cannot be approached without a personal decision to accept or reject a rather simple thesis. The evaluation process generally includes three major components or sources from which to elicit data: an interview with the defendant; traditional and/or forensic assessment instruments; and third-party information, including but not limited to collateral reports, witness statements, victim statements, police reports, and records of various sorts. Research in the area of criminal responsibility has taken a number of forms. Future research that incorporates samples of jury-eligible adults will help further this important body of knowledge.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.177
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0480.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.110
GPT teacher head0.420
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations13
Published2013
Admission routes1
Has abstractyes

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